Genetic correlation between female infertility and mental health and lifestyle factors: A linkage disequilibrium score regression study

Abstract Background and Aims Female fertility is a heterogeneous condition of complete psychosocial and physical well‐being. Observational studies have revealed that women with infertility have varying degrees of poor mental status and lifestyle choices in varying degrees. However, the genetic contribution to female infertility remains elusive. Our study aimed to explore the genetic correlations between female infertility and mental health and lifestyle factors. Methods The genome‐wide association study (GWAS) data sets of characteristics related to mental health and lifestyle were obtained from the IEU OpenGWAS database. The GWAS data sets of female infertility were derived from the Finggen database. Linkage disequilibrium score regression was performed to systematically estimate the pairwise genetic correlations between female infertility and a set of mental health‐ and lifestyle‐related traits. Results The genetic correlation analysis revealed a moderate and positive genetic correlation between depressive symptoms, major depressive disorder, and female infertility. Similarly, worry and the personality trait of neuroticism displayed a moderate positive genetic correlation with female infertility. Adversely, a negative and moderate genetic correlation was observed between strenuous sports or exercises and female infertility. Conclusion The study demonstrated genetic correlations between female infertility and mental health status, including depression, worry, and neuroticism. Additionally, we observed that females with better physical activity may have reduced risks of female infertility. These findings would serve as a fundamental resource for understanding the genetic mechanisms of the effects of mental health and lifestyle factors on female infertility.


| INTRODUCTION
Infertility is defined as failure to achieve pregnancy within 12 months of unprotected intercourse or therapeutic donor insemination in females <35 years or within 6 months of that in females >35 years. 1 With delayed child-bearing age, there has been an increasing infertility rate globally. Infertility affects 10%-25% of couples of fertility age (18-45 years of age) worldwide. [2][3][4] It is a couples' issue; however, the existing treatments for infertility are mainly for women. 3,5 The common reasons for female infertility include tubal obstruction, ovulatory function, and structural abnormalities. 1 The risk factors may impact female infertility variably. The risk factors include age, reduced ovarian reserve, ovulatory dysfunction, menstrual disorders, infertility for >3 years, and endometriosis. 6 However, for females, infertility may have ramifications beyond reproductive health. Female fertility is a state of complete psychosocial and physical well-being. Attention has been drawn to the association between female infertility and mental health disorders and lifestyle. 7 For instance, observational studies have demonstrated that females with infertility have varying degrees of anxiety and depression. [8][9][10] Additionally, infertility is a significant psychological stressor, which may be associated with marital conflict and domestic violence. 11 Poor mental health conditions including depression aggravate infertility. Moreover, a recent review has revealed an association between multiple lifestyle factors, such as weight and exercise, and fertility. 12 Female infertility is a heterogeneous condition, the genetic contribution to which remains elusive. [13][14][15] Genetic correlation, which is the proportion of variance shared by two or more traits owing to common genetic causes, can explain the causes underlying complex diseases and traits. 16 Genome-wide association studies (GWAS), based on a large sample size with an array of data, are a tool for detecting genetic variations associated with the target diseases or traits across the whole genome. 17 Linkage disequilibrium score regression (LDSC), a technique that only requires GWAS summary statistics, has become a popular approach to estimating the genetic correlation of infertility. 18 18 For multiple GWAS data sets available for a single trait, only the largest study was used.
Cases of major depressive disorder, 19 anorexia nervosa, 20 schizophrenia, 21 autism spectrum disorder, 22 and neuroticism 23 were defined by the ICD codes or the Diagnostic and Statistical Manual of Mental Disorders criteria ( Table 1). The trait linked to depressive symptoms was operationalized by combining responses to a series of questions (i.e., the frequency with which the respondent experienced feelings of unenthusiasm/disinterest and depression/hopelessness in the previous 2 weeks). 24 Similarly, worry 25 was evaluated via questions (i.e., "Are you worried?," "Do you suffer from nervousness?," "Would you call yourself a nervous person?," and "Would you describe yourself as tense or highly strung"). The detailed cohort descriptions and information about genotyping, imputation, and association analysis are available in the published studies. Most mental health condition-related GWAS utilized the UK Biobank data set, which is a large population-based study of adults aged 40-69 years residing in England, Scotland, and Wales. 26 Besides the UK Biobank, some of them were obtained from the Psychiatric Genomics Consortium (PGC), Genetic Epidemiology Research on Aging (GERA), and Genetic Consortium for Anorexia Nervosa (GCAN).

| Lifestyle factors
The following lifestyle factors were included in our study: Sleep duration, alcoholic consumption, smoking behavior, vitamin D levels, and strenuous sports or exercises (Table 2). Strenuous sports or exercises, such as fast cycling, aerobics, and heavy lifting, induce sweat or heavy breathing. 29 Lifestyle-related GWAS utilized the UK Biobank data set, GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN), and SUNLIGHT Consortium. Ethical approval and participant consent for each study contributing to the GWAS are available in the original publications.

| Statistical analysis
LDSC analysis is a powerful tool to investigate the shared genetic components (r g , genetic correlation) between common traits or diseases based on GWAS summary statistics. Bivariate LDSC analyses were performed using the LDSC software version 1.0.1 (https://github.com/bulik/ldsc). 16 The LDSC method did not require individual-level genotype data. GWAS summary statistics were used to regress χ 2 statistics on their LD scores. 33 Genetic correlations were calculated using overlapping single-nucleotide polymorphisms (SNPs) from the GWAS summary statistic files. The SNPs with minor allele frequencies >0.01 were included in LDSC.
The Benjamin−Hochberg false discovery rate (FDR) correction was obtained to account for multiple testing, and the FDR-adjusted p < 0.05 was considered significant. All statistical tests were twosided. The analyses were performed using R version 3.6.3 statistical software (R Foundation for Statistical Computing; https://www.R-project.org/).

| Genetic correlations between female infertility and mental health disorders
The genetic correlations (r g ) and standard errors between female infertility and traits of mental health status are presented in Figure 1.   F I G U R E 1 Genetic correlations (r g ) between mental health-related traits and female infertility. Genetic correlation estimates obtained through linkage disequilibrium score regression are presented; false discovery rate (FDR)-adjusted p value is listed on the right. Error bars represent standard error.
F I G U R E 2 Genetic correlations (r g ) between lifestyle-related traits and female infertility. Genetic correlation estimates obtained through linkage disequilibrium score regression are presented; false discovery rate (FDR)-adjusted p value is listed on the right. Error bars represent standard error.

| DISCUSSION
To our knowledge, our study is the first to investigate genetic correlations between female infertility and multiple mental healthrelated traits and lifestyle factors. We demonstrated significant genetic correlations between female infertility and poor mental health and strenuous sports Previous studies have demonstrated that women with infertility reported elevated levels of anxiety and depression. [8][9][10] In this study, female infertility revealed significant shared genetic components with a range of mental health-related traits, consistent with previous epidemiological reports. A different interpretation of the effect of depressive symptoms and major depressive disorder on female infertility is required owing to the genetic overlap between depression and female infertility. The SNPs that predispose to depression also increase the risk for depression and major depressive disorder, which should be considered in addition to female infertility.
This study supports previous studies displaying that depression is associated with increased reporting and sensitivity to female infertility. The genetic correlation between worry and female infertility observed in our study demonstrated that anxiety increases the risk for infertility and displayed the genetic influences on anxiety and female infertility.
We observed a significant genetic correlation between neuroticism and female infertility, which has been reported in previous observational studies. 34,35 Females via personality traits, such as neuroticism, prime them to respond negatively to fertility. These results indicated that there is some genetic overlap between neuroticism and female infertility at the molecular level. It has been reported that the genetic signal of neuroticism partly originates from two genetically distinguishable subclusters ("depressed affect" and "worry"). 25 We examined genetic correlations between depressive symptoms, worry, and female infertility. We observed depression to be more strongly associated with female infertility than worry. This further suggested that the genetic association may be stronger between female infertility and depression than between female infertility and worry.
We observed a modest and significant genetic correlation between strenuous exercise and female infertility using the LDSC approach. The negative correlation reflected a difference in genetic background between female infertility and strenuous exercises.
Physically active females were more likely to be fertile, consistent with a previous observational study reporting that more females with normal fertility engaged in moderate and vigorous activities. 36 However, other conservative studies have revealed that among females, excessive exercise may lead to hypothalamic amenorrhea, causing short-term infertility. 37 Generally, levels of engagement in physical activity vary across individuals. Recall, comprehension, and social desirability bias are prevalent in the measurement of physical activity, whereas phenotypic agreement in strenuous and excessive exercises is generally poor between subjective and objective measures. 38 Interindividual variation is likely to exist in relevant studies. Regardless, our data did not provide any insight into the impact of light or moderate physical activity on infertility.
Smoking phenotypes and alcohol use are genetically correlated with many health conditions. 39 In our study, we did not detect a significant correlation between alcohol use and smoking and female infertility. Evidence has revealed a significant association between smoking and reduced female fertility. 39 However, the impact of alcohol use on female fertility is inconsistent. Some studies have revealed that moderate alcohol use may be unrelated to female fertility albeit increased the risk of adverse pregnancy outcomes. 39,40 Although there was no genetic overlap between smoking, alcohol use, and female infertility, the adverse effects of smoking and alcohol use should be further evaluated.
There were several strengths to our study. First, our study addressed the genetic correlation between female infertility and other diseases or traits. Second, we believe that LDSC is the most effective tool for genetic correlation analysis since the summary statistics are available for much larger sample sizes than those with individual genotype data. Third, in contrast to observational studies, our LDSC analysis supported a reliable methodology to assess the association between complex traits while minimizing the possibility of bias owing to unknown confounding. This study had some limitations.
We did not estimate sex-based genetic correlation. Female infertility phenotypes only included females, whereas samples of mental health disorders and lifestyle-related factors included both sexes. Furthermore, most phenotypes used the UK Biobank data, which limits the generalizability of results to other ancestries. For a larger sample size, large-scale genetic studies that replicate findings across other ancestry groups will be useful. Finally, the clinical significance of some poor or modest genetic correlations remained elusive. Future work with an increased sample size to replicate these findings is warranted.

| CONCLUSION
Conclusively, by utilizing the LDSC approach, we evaluated the association between mental health status, lifestyle factors, and female infertility. Our study identified significant and positive genetic correlations between multiple mental health conditions and female infertility. We also revealed that strenuous exercise is negatively correlated with female infertility. We believe that our findings would serve as a fundamental resource for understanding the genetic mechanisms of the effects of depression, anxiety, neuroticism, and lifestyle factors on female infertility. Furthermore, we believe that our study lends support to future research into the mental health and lifestyle of females with infertility.